Overview

Dataset statistics

Number of variables18
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory144.0 B

Variable types

Numeric8
Categorical10

Alerts

Surname has a high cardinality: 2932 distinct valuesHigh cardinality
Exited is highly overall correlated with ComplainHigh correlation
Complain is highly overall correlated with ExitedHigh correlation
RowNumber is uniformly distributedUniform
RowNumber has unique valuesUnique
CustomerId has unique valuesUnique
Tenure has 413 (4.1%) zerosZeros
Balance has 3617 (36.2%) zerosZeros

Reproduction

Analysis started2024-05-27 21:52:37.513822
Analysis finished2024-05-27 21:53:05.985524
Duration28.47 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

RowNumber
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5000.5
Minimum1
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2024-05-27T22:53:07.516014image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile500.95
Q12500.75
median5000.5
Q37500.25
95-th percentile9500.05
Maximum10000
Range9999
Interquartile range (IQR)4999.5

Descriptive statistics

Standard deviation2886.8957
Coefficient of variation (CV)0.5773214
Kurtosis-1.2
Mean5000.5
Median Absolute Deviation (MAD)2500
Skewness0
Sum50005000
Variance8334166.7
MonotonicityStrictly increasing
2024-05-27T22:53:07.881572image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
6671 1
 
< 0.1%
6664 1
 
< 0.1%
6665 1
 
< 0.1%
6666 1
 
< 0.1%
6667 1
 
< 0.1%
6668 1
 
< 0.1%
6669 1
 
< 0.1%
6670 1
 
< 0.1%
6672 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
10000 1
< 0.1%
9999 1
< 0.1%
9998 1
< 0.1%
9997 1
< 0.1%
9996 1
< 0.1%
9995 1
< 0.1%
9994 1
< 0.1%
9993 1
< 0.1%
9992 1
< 0.1%
9991 1
< 0.1%

CustomerId
Real number (ℝ)

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15690941
Minimum15565701
Maximum15815690
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2024-05-27T22:53:08.253425image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum15565701
5-th percentile15578824
Q115628528
median15690738
Q315753234
95-th percentile15803034
Maximum15815690
Range249989
Interquartile range (IQR)124705.5

Descriptive statistics

Standard deviation71936.186
Coefficient of variation (CV)0.0045845681
Kurtosis-1.1961125
Mean15690941
Median Absolute Deviation (MAD)62432.5
Skewness0.0011491459
Sum1.5690941 × 1011
Variance5.1748149 × 109
MonotonicityNot monotonic
2024-05-27T22:53:08.621483image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15634602 1
 
< 0.1%
15667932 1
 
< 0.1%
15766185 1
 
< 0.1%
15667632 1
 
< 0.1%
15599024 1
 
< 0.1%
15798709 1
 
< 0.1%
15741921 1
 
< 0.1%
15793671 1
 
< 0.1%
15797900 1
 
< 0.1%
15795933 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
15565701 1
< 0.1%
15565706 1
< 0.1%
15565714 1
< 0.1%
15565779 1
< 0.1%
15565796 1
< 0.1%
15565806 1
< 0.1%
15565878 1
< 0.1%
15565879 1
< 0.1%
15565891 1
< 0.1%
15565996 1
< 0.1%
ValueCountFrequency (%)
15815690 1
< 0.1%
15815660 1
< 0.1%
15815656 1
< 0.1%
15815645 1
< 0.1%
15815628 1
< 0.1%
15815626 1
< 0.1%
15815615 1
< 0.1%
15815560 1
< 0.1%
15815552 1
< 0.1%
15815534 1
< 0.1%

Surname
Categorical

Distinct2932
Distinct (%)29.3%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
Smith
 
32
Scott
 
29
Martin
 
29
Walker
 
28
Brown
 
26
Other values (2927)
9856 

Length

Max length23
Median length16
Mean length6.4349
Min length2

Characters and Unicode

Total characters64349
Distinct characters55
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1558 ?
Unique (%)15.6%

Sample

1st rowHargrave
2nd rowHill
3rd rowOnio
4th rowBoni
5th rowMitchell

Common Values

ValueCountFrequency (%)
Smith 32
 
0.3%
Scott 29
 
0.3%
Martin 29
 
0.3%
Walker 28
 
0.3%
Brown 26
 
0.3%
Yeh 25
 
0.2%
Shih 25
 
0.2%
Genovese 25
 
0.2%
Maclean 24
 
0.2%
Wright 24
 
0.2%
Other values (2922) 9733
97.3%

Length

2024-05-27T22:53:08.975825image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
lo 33
 
0.3%
smith 32
 
0.3%
martin 29
 
0.3%
scott 29
 
0.3%
walker 28
 
0.3%
brown 26
 
0.3%
yeh 25
 
0.2%
shih 25
 
0.2%
genovese 25
 
0.2%
maclean 24
 
0.2%
Other values (2931) 9779
97.3%

Most occurring characters

ValueCountFrequency (%)
a 5799
 
9.0%
e 5764
 
9.0%
n 5235
 
8.1%
o 4905
 
7.6%
i 4491
 
7.0%
r 3547
 
5.5%
l 2921
 
4.5%
s 2592
 
4.0%
u 2552
 
4.0%
h 2150
 
3.3%
Other values (45) 24393
37.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 53647
83.4%
Uppercase Letter 10299
 
16.0%
Other Punctuation 329
 
0.5%
Space Separator 55
 
0.1%
Dash Punctuation 19
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 5799
10.8%
e 5764
10.7%
n 5235
 
9.8%
o 4905
 
9.1%
i 4491
 
8.4%
r 3547
 
6.6%
l 2921
 
5.4%
s 2592
 
4.8%
u 2552
 
4.8%
h 2150
 
4.0%
Other values (16) 13691
25.5%
Uppercase Letter
ValueCountFrequency (%)
C 1106
 
10.7%
M 1004
 
9.7%
B 707
 
6.9%
S 685
 
6.7%
H 661
 
6.4%
T 573
 
5.6%
L 545
 
5.3%
W 481
 
4.7%
P 466
 
4.5%
G 442
 
4.3%
Other values (15) 3629
35.2%
Other Punctuation
ValueCountFrequency (%)
' 237
72.0%
? 92
 
28.0%
Space Separator
ValueCountFrequency (%)
55
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 63946
99.4%
Common 403
 
0.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 5799
 
9.1%
e 5764
 
9.0%
n 5235
 
8.2%
o 4905
 
7.7%
i 4491
 
7.0%
r 3547
 
5.5%
l 2921
 
4.6%
s 2592
 
4.1%
u 2552
 
4.0%
h 2150
 
3.4%
Other values (41) 23990
37.5%
Common
ValueCountFrequency (%)
' 237
58.8%
? 92
 
22.8%
55
 
13.6%
- 19
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 64349
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 5799
 
9.0%
e 5764
 
9.0%
n 5235
 
8.1%
o 4905
 
7.6%
i 4491
 
7.0%
r 3547
 
5.5%
l 2921
 
4.5%
s 2592
 
4.0%
u 2552
 
4.0%
h 2150
 
3.3%
Other values (45) 24393
37.9%

CreditScore
Real number (ℝ)

Distinct460
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean650.5288
Minimum350
Maximum850
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2024-05-27T22:53:09.313418image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum350
5-th percentile489
Q1584
median652
Q3718
95-th percentile812
Maximum850
Range500
Interquartile range (IQR)134

Descriptive statistics

Standard deviation96.653299
Coefficient of variation (CV)0.14857651
Kurtosis-0.42572568
Mean650.5288
Median Absolute Deviation (MAD)67
Skewness-0.071606608
Sum6505288
Variance9341.8602
MonotonicityNot monotonic
2024-05-27T22:53:09.918299image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
850 233
 
2.3%
678 63
 
0.6%
655 54
 
0.5%
705 53
 
0.5%
667 53
 
0.5%
684 52
 
0.5%
670 50
 
0.5%
651 50
 
0.5%
683 48
 
0.5%
652 48
 
0.5%
Other values (450) 9296
93.0%
ValueCountFrequency (%)
350 5
0.1%
351 1
 
< 0.1%
358 1
 
< 0.1%
359 1
 
< 0.1%
363 1
 
< 0.1%
365 1
 
< 0.1%
367 1
 
< 0.1%
373 1
 
< 0.1%
376 2
 
< 0.1%
382 1
 
< 0.1%
ValueCountFrequency (%)
850 233
2.3%
849 8
 
0.1%
848 5
 
0.1%
847 6
 
0.1%
846 5
 
0.1%
845 6
 
0.1%
844 7
 
0.1%
843 2
 
< 0.1%
842 7
 
0.1%
841 12
 
0.1%

Geography
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
France
5014 
Germany
2509 
Spain
2477 

Length

Max length7
Median length6
Mean length6.0032
Min length5

Characters and Unicode

Total characters60032
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFrance
2nd rowSpain
3rd rowFrance
4th rowFrance
5th rowSpain

Common Values

ValueCountFrequency (%)
France 5014
50.1%
Germany 2509
25.1%
Spain 2477
24.8%

Length

2024-05-27T22:53:10.238145image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-27T22:53:10.673139image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
france 5014
50.1%
germany 2509
25.1%
spain 2477
24.8%

Most occurring characters

ValueCountFrequency (%)
a 10000
16.7%
n 10000
16.7%
r 7523
12.5%
e 7523
12.5%
F 5014
8.4%
c 5014
8.4%
G 2509
 
4.2%
m 2509
 
4.2%
y 2509
 
4.2%
S 2477
 
4.1%
Other values (2) 4954
8.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 50032
83.3%
Uppercase Letter 10000
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 10000
20.0%
n 10000
20.0%
r 7523
15.0%
e 7523
15.0%
c 5014
10.0%
m 2509
 
5.0%
y 2509
 
5.0%
p 2477
 
5.0%
i 2477
 
5.0%
Uppercase Letter
ValueCountFrequency (%)
F 5014
50.1%
G 2509
25.1%
S 2477
24.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 60032
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 10000
16.7%
n 10000
16.7%
r 7523
12.5%
e 7523
12.5%
F 5014
8.4%
c 5014
8.4%
G 2509
 
4.2%
m 2509
 
4.2%
y 2509
 
4.2%
S 2477
 
4.1%
Other values (2) 4954
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60032
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 10000
16.7%
n 10000
16.7%
r 7523
12.5%
e 7523
12.5%
F 5014
8.4%
c 5014
8.4%
G 2509
 
4.2%
m 2509
 
4.2%
y 2509
 
4.2%
S 2477
 
4.1%
Other values (2) 4954
8.3%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
Male
5457 
Female
4543 

Length

Max length6
Median length4
Mean length4.9086
Min length4

Characters and Unicode

Total characters49086
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowFemale
4th rowFemale
5th rowFemale

Common Values

ValueCountFrequency (%)
Male 5457
54.6%
Female 4543
45.4%

Length

2024-05-27T22:53:11.024949image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-27T22:53:11.322513image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
male 5457
54.6%
female 4543
45.4%

Most occurring characters

ValueCountFrequency (%)
e 14543
29.6%
a 10000
20.4%
l 10000
20.4%
M 5457
 
11.1%
F 4543
 
9.3%
m 4543
 
9.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 39086
79.6%
Uppercase Letter 10000
 
20.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 14543
37.2%
a 10000
25.6%
l 10000
25.6%
m 4543
 
11.6%
Uppercase Letter
ValueCountFrequency (%)
M 5457
54.6%
F 4543
45.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 49086
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 14543
29.6%
a 10000
20.4%
l 10000
20.4%
M 5457
 
11.1%
F 4543
 
9.3%
m 4543
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49086
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 14543
29.6%
a 10000
20.4%
l 10000
20.4%
M 5457
 
11.1%
F 4543
 
9.3%
m 4543
 
9.3%

Age
Real number (ℝ)

Distinct70
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.9218
Minimum18
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2024-05-27T22:53:11.602799image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile25
Q132
median37
Q344
95-th percentile60
Maximum92
Range74
Interquartile range (IQR)12

Descriptive statistics

Standard deviation10.487806
Coefficient of variation (CV)0.26945841
Kurtosis1.3953471
Mean38.9218
Median Absolute Deviation (MAD)6
Skewness1.0113203
Sum389218
Variance109.99408
MonotonicityNot monotonic
2024-05-27T22:53:11.959015image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37 478
 
4.8%
38 477
 
4.8%
35 474
 
4.7%
36 456
 
4.6%
34 447
 
4.5%
33 442
 
4.4%
40 432
 
4.3%
39 423
 
4.2%
32 418
 
4.2%
31 404
 
4.0%
Other values (60) 5549
55.5%
ValueCountFrequency (%)
18 22
 
0.2%
19 27
 
0.3%
20 40
 
0.4%
21 53
 
0.5%
22 84
0.8%
23 99
1.0%
24 132
1.3%
25 154
1.5%
26 200
2.0%
27 209
2.1%
ValueCountFrequency (%)
92 2
 
< 0.1%
88 1
 
< 0.1%
85 1
 
< 0.1%
84 2
 
< 0.1%
83 1
 
< 0.1%
82 1
 
< 0.1%
81 4
< 0.1%
80 3
< 0.1%
79 4
< 0.1%
78 5
0.1%

Tenure
Real number (ℝ)

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0128
Minimum0
Maximum10
Zeros413
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2024-05-27T22:53:12.281918image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q37
95-th percentile9
Maximum10
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.8921744
Coefficient of variation (CV)0.57695786
Kurtosis-1.1652252
Mean5.0128
Median Absolute Deviation (MAD)2
Skewness0.010991458
Sum50128
Variance8.3646726
MonotonicityNot monotonic
2024-05-27T22:53:12.546414image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2 1048
10.5%
1 1035
10.3%
7 1028
10.3%
8 1025
10.2%
5 1012
10.1%
3 1009
10.1%
4 989
9.9%
9 984
9.8%
6 967
9.7%
10 490
4.9%
ValueCountFrequency (%)
0 413
 
4.1%
1 1035
10.3%
2 1048
10.5%
3 1009
10.1%
4 989
9.9%
5 1012
10.1%
6 967
9.7%
7 1028
10.3%
8 1025
10.2%
9 984
9.8%
ValueCountFrequency (%)
10 490
4.9%
9 984
9.8%
8 1025
10.2%
7 1028
10.3%
6 967
9.7%
5 1012
10.1%
4 989
9.9%
3 1009
10.1%
2 1048
10.5%
1 1035
10.3%

Balance
Real number (ℝ)

Distinct6382
Distinct (%)63.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76485.889
Minimum0
Maximum250898.09
Zeros3617
Zeros (%)36.2%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2024-05-27T22:53:12.858949image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median97198.54
Q3127644.24
95-th percentile162711.67
Maximum250898.09
Range250898.09
Interquartile range (IQR)127644.24

Descriptive statistics

Standard deviation62397.405
Coefficient of variation (CV)0.81580283
Kurtosis-1.4894118
Mean76485.889
Median Absolute Deviation (MAD)46766.79
Skewness-0.14110871
Sum7.6485889 × 108
Variance3.8934362 × 109
MonotonicityNot monotonic
2024-05-27T22:53:13.238212image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3617
36.2%
130170.82 2
 
< 0.1%
105473.74 2
 
< 0.1%
85304.27 1
 
< 0.1%
159397.75 1
 
< 0.1%
144238.7 1
 
< 0.1%
112262.84 1
 
< 0.1%
109106.8 1
 
< 0.1%
142147.32 1
 
< 0.1%
109109.33 1
 
< 0.1%
Other values (6372) 6372
63.7%
ValueCountFrequency (%)
0 3617
36.2%
3768.69 1
 
< 0.1%
12459.19 1
 
< 0.1%
14262.8 1
 
< 0.1%
16893.59 1
 
< 0.1%
23503.31 1
 
< 0.1%
24043.45 1
 
< 0.1%
27288.43 1
 
< 0.1%
27517.15 1
 
< 0.1%
27755.97 1
 
< 0.1%
ValueCountFrequency (%)
250898.09 1
< 0.1%
238387.56 1
< 0.1%
222267.63 1
< 0.1%
221532.8 1
< 0.1%
216109.88 1
< 0.1%
214346.96 1
< 0.1%
213146.2 1
< 0.1%
212778.2 1
< 0.1%
212696.32 1
< 0.1%
212692.97 1
< 0.1%

NumOfProducts
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
1
5084 
2
4590 
3
 
266
4
 
60

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row3
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 5084
50.8%
2 4590
45.9%
3 266
 
2.7%
4 60
 
0.6%

Length

2024-05-27T22:53:13.539355image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-27T22:53:13.817522image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 5084
50.8%
2 4590
45.9%
3 266
 
2.7%
4 60
 
0.6%

Most occurring characters

ValueCountFrequency (%)
1 5084
50.8%
2 4590
45.9%
3 266
 
2.7%
4 60
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5084
50.8%
2 4590
45.9%
3 266
 
2.7%
4 60
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5084
50.8%
2 4590
45.9%
3 266
 
2.7%
4 60
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5084
50.8%
2 4590
45.9%
3 266
 
2.7%
4 60
 
0.6%

HasCrCard
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
1
7055 
0
2945 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 7055
70.5%
0 2945
29.4%

Length

2024-05-27T22:53:14.065002image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-27T22:53:14.328217image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 7055
70.5%
0 2945
29.4%

Most occurring characters

ValueCountFrequency (%)
1 7055
70.5%
0 2945
29.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7055
70.5%
0 2945
29.4%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 7055
70.5%
0 2945
29.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 7055
70.5%
0 2945
29.4%

IsActiveMember
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
1
5151 
0
4849 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 5151
51.5%
0 4849
48.5%

Length

2024-05-27T22:53:14.553586image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-27T22:53:14.859996image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 5151
51.5%
0 4849
48.5%

Most occurring characters

ValueCountFrequency (%)
1 5151
51.5%
0 4849
48.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5151
51.5%
0 4849
48.5%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5151
51.5%
0 4849
48.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5151
51.5%
0 4849
48.5%

EstimatedSalary
Real number (ℝ)

Distinct9999
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100090.24
Minimum11.58
Maximum199992.48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2024-05-27T22:53:15.191720image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum11.58
5-th percentile9851.8185
Q151002.11
median100193.91
Q3149388.25
95-th percentile190155.38
Maximum199992.48
Range199980.9
Interquartile range (IQR)98386.137

Descriptive statistics

Standard deviation57510.493
Coefficient of variation (CV)0.57458642
Kurtosis-1.1815184
Mean100090.24
Median Absolute Deviation (MAD)49198.15
Skewness0.0020853577
Sum1.0009024 × 109
Variance3.3074568 × 109
MonotonicityNot monotonic
2024-05-27T22:53:15.604028image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24924.92 2
 
< 0.1%
101348.88 1
 
< 0.1%
55313.44 1
 
< 0.1%
72500.68 1
 
< 0.1%
182692.8 1
 
< 0.1%
4993.94 1
 
< 0.1%
124964.82 1
 
< 0.1%
161971.42 1
 
< 0.1%
39488.04 1
 
< 0.1%
187811.71 1
 
< 0.1%
Other values (9989) 9989
99.9%
ValueCountFrequency (%)
11.58 1
< 0.1%
90.07 1
< 0.1%
91.75 1
< 0.1%
96.27 1
< 0.1%
106.67 1
< 0.1%
123.07 1
< 0.1%
142.81 1
< 0.1%
143.34 1
< 0.1%
178.19 1
< 0.1%
216.27 1
< 0.1%
ValueCountFrequency (%)
199992.48 1
< 0.1%
199970.74 1
< 0.1%
199953.33 1
< 0.1%
199929.17 1
< 0.1%
199909.32 1
< 0.1%
199862.75 1
< 0.1%
199857.47 1
< 0.1%
199841.32 1
< 0.1%
199808.1 1
< 0.1%
199805.63 1
< 0.1%

Exited
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
7962 
1
2038 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7962
79.6%
1 2038
 
20.4%

Length

2024-05-27T22:53:15.948282image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-27T22:53:16.212691image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 7962
79.6%
1 2038
 
20.4%

Most occurring characters

ValueCountFrequency (%)
0 7962
79.6%
1 2038
 
20.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7962
79.6%
1 2038
 
20.4%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7962
79.6%
1 2038
 
20.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7962
79.6%
1 2038
 
20.4%

Complain
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
7956 
1
2044 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7956
79.6%
1 2044
 
20.4%

Length

2024-05-27T22:53:16.437175image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-27T22:53:16.707376image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 7956
79.6%
1 2044
 
20.4%

Most occurring characters

ValueCountFrequency (%)
0 7956
79.6%
1 2044
 
20.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7956
79.6%
1 2044
 
20.4%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7956
79.6%
1 2044
 
20.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7956
79.6%
1 2044
 
20.4%
Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
3
2042 
2
2014 
4
2008 
5
2004 
1
1932 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row3
4th row5
5th row5

Common Values

ValueCountFrequency (%)
3 2042
20.4%
2 2014
20.1%
4 2008
20.1%
5 2004
20.0%
1 1932
19.3%

Length

2024-05-27T22:53:16.961832image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-27T22:53:17.264843image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
3 2042
20.4%
2 2014
20.1%
4 2008
20.1%
5 2004
20.0%
1 1932
19.3%

Most occurring characters

ValueCountFrequency (%)
3 2042
20.4%
2 2014
20.1%
4 2008
20.1%
5 2004
20.0%
1 1932
19.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 2042
20.4%
2 2014
20.1%
4 2008
20.1%
5 2004
20.0%
1 1932
19.3%

Most occurring scripts

ValueCountFrequency (%)
Common 10000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 2042
20.4%
2 2014
20.1%
4 2008
20.1%
5 2004
20.0%
1 1932
19.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 2042
20.4%
2 2014
20.1%
4 2008
20.1%
5 2004
20.0%
1 1932
19.3%

Card Type
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
DIAMOND
2507 
GOLD
2502 
SILVER
2496 
PLATINUM
2495 

Length

Max length8
Median length7
Mean length6.2493
Min length4

Characters and Unicode

Total characters62493
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDIAMOND
2nd rowDIAMOND
3rd rowDIAMOND
4th rowGOLD
5th rowGOLD

Common Values

ValueCountFrequency (%)
DIAMOND 2507
25.1%
GOLD 2502
25.0%
SILVER 2496
25.0%
PLATINUM 2495
24.9%

Length

2024-05-27T22:53:17.564874image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-27T22:53:17.909407image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
diamond 2507
25.1%
gold 2502
25.0%
silver 2496
25.0%
platinum 2495
24.9%

Most occurring characters

ValueCountFrequency (%)
D 7516
12.0%
I 7498
12.0%
L 7493
12.0%
O 5009
8.0%
A 5002
8.0%
M 5002
8.0%
N 5002
8.0%
G 2502
 
4.0%
S 2496
 
4.0%
V 2496
 
4.0%
Other values (5) 12477
20.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 62493
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 7516
12.0%
I 7498
12.0%
L 7493
12.0%
O 5009
8.0%
A 5002
8.0%
M 5002
8.0%
N 5002
8.0%
G 2502
 
4.0%
S 2496
 
4.0%
V 2496
 
4.0%
Other values (5) 12477
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 62493
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 7516
12.0%
I 7498
12.0%
L 7493
12.0%
O 5009
8.0%
A 5002
8.0%
M 5002
8.0%
N 5002
8.0%
G 2502
 
4.0%
S 2496
 
4.0%
V 2496
 
4.0%
Other values (5) 12477
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 62493
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 7516
12.0%
I 7498
12.0%
L 7493
12.0%
O 5009
8.0%
A 5002
8.0%
M 5002
8.0%
N 5002
8.0%
G 2502
 
4.0%
S 2496
 
4.0%
V 2496
 
4.0%
Other values (5) 12477
20.0%

Point Earned
Real number (ℝ)

Distinct785
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean606.5151
Minimum119
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2024-05-27T22:53:18.240464image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum119
5-th percentile255
Q1410
median605
Q3801
95-th percentile960
Maximum1000
Range881
Interquartile range (IQR)391

Descriptive statistics

Standard deviation225.92484
Coefficient of variation (CV)0.37249664
Kurtosis-1.193781
Mean606.5151
Median Absolute Deviation (MAD)195
Skewness0.008344113
Sum6065151
Variance51042.033
MonotonicityNot monotonic
2024-05-27T22:53:18.603389image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
408 26
 
0.3%
709 25
 
0.2%
244 23
 
0.2%
629 23
 
0.2%
503 22
 
0.2%
343 22
 
0.2%
564 22
 
0.2%
351 22
 
0.2%
240 22
 
0.2%
720 21
 
0.2%
Other values (775) 9772
97.7%
ValueCountFrequency (%)
119 1
 
< 0.1%
163 1
 
< 0.1%
206 1
 
< 0.1%
219 16
0.2%
220 7
0.1%
221 14
0.1%
222 11
0.1%
223 12
0.1%
224 9
0.1%
225 14
0.1%
ValueCountFrequency (%)
1000 13
0.1%
999 7
 
0.1%
998 12
0.1%
997 15
0.1%
996 2
 
< 0.1%
995 19
0.2%
994 17
0.2%
993 12
0.1%
992 13
0.1%
991 11
0.1%

Interactions

2024-05-27T22:53:02.275383image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:52:43.286020image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:52:46.409344image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:52:48.808428image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:52:51.767767image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:52:54.758854image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:52:57.322614image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:52:59.718704image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:53:02.556011image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:52:43.662297image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:52:46.698138image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:52:49.114968image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:52:52.117408image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:52:55.035905image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:52:57.618540image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:53:00.034806image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:53:02.833520image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:52:44.645369image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:52:46.973493image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:52:49.395457image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:52:52.474823image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:52:55.328040image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:52:57.916489image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:53:00.362817image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:53:03.103030image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:52:44.921983image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:52:47.248653image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:52:49.662327image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:52:52.790156image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:52:55.633532image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:52:58.197391image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:53:00.660367image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:53:03.415060image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:52:45.218396image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:52:47.552763image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:52:50.052269image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:52:53.232865image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:52:55.963726image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:52:58.512942image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:53:00.995323image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:53:03.701437image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:52:45.509060image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:52:47.907218image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:52:50.602870image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:52:53.662427image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:52:56.440797image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:52:58.842561image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:53:01.316267image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:53:03.973429image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:52:45.795554image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:52:48.204819image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:52:51.012442image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:52:54.077265image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:52:56.721491image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:52:59.128722image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:53:01.628035image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:53:04.295543image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:52:46.131767image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:52:48.519965image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:52:51.448831image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:52:54.461326image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:52:57.043161image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:52:59.448346image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-05-27T22:53:01.978362image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2024-05-27T22:53:19.125859image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
RowNumberCustomerIdCreditScoreAgeTenureBalanceEstimatedSalaryPoint EarnedGeographyGenderNumOfProductsHasCrCardIsActiveMemberExitedComplainSatisfaction ScoreCard Type
RowNumber1.0000.0040.0050.000-0.007-0.009-0.0060.0020.0180.0000.0090.0080.0000.0000.0000.0100.004
CustomerId0.0041.0000.0060.009-0.015-0.0140.015-0.0130.0000.0000.0060.0000.0110.0220.0230.0000.004
CreditScore0.0050.0061.000-0.0080.0010.0060.0010.0010.0180.0000.0170.0000.0250.0860.0860.0000.000
Age0.0000.009-0.0081.000-0.0100.033-0.002-0.0010.0500.0260.0870.0130.1440.3750.3730.0130.000
Tenure-0.007-0.0150.001-0.0101.000-0.0100.008-0.0100.0280.0250.0350.0260.0210.0220.0230.0080.000
Balance-0.009-0.0140.0060.033-0.0101.0000.0120.0130.3150.0000.2300.0390.0140.1400.1390.0120.006
EstimatedSalary-0.0060.0150.001-0.0020.0080.0121.000-0.0020.0170.0210.0190.0000.0250.0000.0000.0170.000
Point Earned0.002-0.0130.001-0.001-0.0100.013-0.0021.0000.0160.0000.0000.0000.0000.0000.0070.0140.020
Geography0.0180.0000.0180.0500.0280.3150.0170.0161.0000.0220.0470.0050.0180.1730.1750.0000.000
Gender0.0000.0000.0000.0260.0250.0000.0210.0000.0221.0000.0420.0000.0200.1060.1060.0000.030
NumOfProducts0.0090.0060.0170.0870.0350.2300.0190.0000.0470.0421.0000.0000.0380.3870.3850.0000.014
HasCrCard0.0080.0000.0000.0130.0260.0390.0000.0000.0050.0000.0001.0000.0060.0000.0000.0000.000
IsActiveMember0.0000.0110.0250.1440.0210.0140.0250.0000.0180.0200.0380.0061.0000.1560.1540.0040.015
Exited0.0000.0220.0860.3750.0220.1400.0000.0000.1730.1060.3870.0000.1561.0000.9950.0000.014
Complain0.0000.0230.0860.3730.0230.1390.0000.0070.1750.1060.3850.0000.1540.9951.0000.0000.014
Satisfaction Score0.0100.0000.0000.0130.0080.0120.0170.0140.0000.0000.0000.0000.0040.0000.0001.0000.000
Card Type0.0040.0040.0000.0000.0000.0060.0000.0200.0000.0300.0140.0000.0150.0140.0140.0001.000

Missing values

2024-05-27T22:53:04.739223image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-27T22:53:05.555464image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

RowNumberCustomerIdSurnameCreditScoreGeographyGenderAgeTenureBalanceNumOfProductsHasCrCardIsActiveMemberEstimatedSalaryExitedComplainSatisfaction ScoreCard TypePoint Earned
0115634602Hargrave619FranceFemale4220.00111101348.88112DIAMOND464
1215647311Hill608SpainFemale41183807.86101112542.58013DIAMOND456
2315619304Onio502FranceFemale428159660.80310113931.57113DIAMOND377
3415701354Boni699FranceFemale3910.0020093826.63005GOLD350
4515737888Mitchell850SpainFemale432125510.8211179084.10005GOLD425
5615574012Chu645SpainMale448113755.78210149756.71115DIAMOND484
6715592531Bartlett822FranceMale5070.0021110062.80002SILVER206
7815656148Obinna376GermanyFemale294115046.74410119346.88112DIAMOND282
8915792365He501FranceMale444142051.0720174940.50003GOLD251
91015592389H?684FranceMale272134603.8811171725.73003GOLD342
RowNumberCustomerIdSurnameCreditScoreGeographyGenderAgeTenureBalanceNumOfProductsHasCrCardIsActiveMemberEstimatedSalaryExitedComplainSatisfaction ScoreCard TypePoint Earned
9990999115798964Nkemakonam714GermanyMale33335016.6011053667.08003GOLD791
9991999215769959Ajuluchukwu597FranceFemale53488381.2111069384.71113GOLD369
9992999315657105Chukwualuka726SpainMale3620.00110195192.40005SILVER560
9993999415569266Rahman644FranceMale287155060.4111029179.52005DIAMOND715
9994999515719294Wood800FranceFemale2920.00200167773.55004PLATINUM311
9995999615606229Obijiaku771FranceMale3950.0021096270.64001DIAMOND300
9996999715569892Johnstone516FranceMale351057369.61111101699.77005PLATINUM771
9997999815584532Liu709FranceFemale3670.0010142085.58113SILVER564
9998999915682355Sabbatini772GermanyMale42375075.3121092888.52112GOLD339
99991000015628319Walker792FranceFemale284130142.7911038190.78003DIAMOND911